Regulated clinical AI
Evidence infrastructure for AI-enabled medical products.
Glacis helps clinical AI teams generate runtime evidence for PCCP-ready change records, post-market monitoring, drift review, model-change records, and control-execution proof — without moving sensitive clinical data out of their infrastructure.
Why now
Regulated AI medical products need proof from real operation, not after-the-fact documentation.
AI medical products change, drift, touch clinical workflows, and generate outputs that reviewers and health-system buyers will question. Screenshots and retrospective logs are weak evidence when the important question is whether the right controls ran at the right time.
Glacis turns consequential runtime events into signed receipts, then assembles those receipts into evidence packs for regulatory review, PCCP updates, post-market monitoring, and internal quality review.
What gets instrumented
Runtime evidence for the AI lifecycle.
Model-change evidence
Version, policy, threshold, and deployment context tied to the behavior that changed.
Control execution
Which guardrail, review rule, redaction, escalation, or block executed at decision time.
Drift and near misses
Operational patterns that show where performance, population, or workflow behavior is moving.
Post-market proof
Receipts that support lifecycle management, health-system review, and audit readiness.
Runtime artifact
Receipts first. Evidence packs second.
Receipts are generated at runtime. Evidence packs are assembled from receipts.
That distinction keeps the evidence grounded in what the system actually did, not in a document created after the fact.
Sensitive environments
Built for PHI and proprietary clinical context.
Glacis generates runtime evidence that controls executed and model behavior stayed within defined boundaries without moving sensitive clinical payloads out of your environment. It records verification metadata, control outcomes, model/version context, threshold decisions, drift signals, and evidence commitments designed to support review without exposing protected clinical content.
Runtime controls
Observe, allow, block, redact, escalate, or require review at the AI boundary.
Signed evidence
Every consequential decision can carry tamper-evident proof.
Zero sensitive-data egress
Proof can travel without exporting prompts, outputs, PHI, customer data, or proprietary context.
Bring one regulated AI workflow.
We’ll map the runtime evidence your clinical AI product needs for change records, post-market monitoring, drift review, and control-execution proof.
Assess clinical AI evidence readiness